@inproceedings{shi-etal-2023-self,
title = "Self-imitation Learning for Action Generation in Text-based Games",
author = "Shi, Zijing and
Xu, Yunqiu and
Fang, Meng and
Chen, Ling",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.50",
doi = "10.18653/v1/2023.eacl-main.50",
pages = "703--726",
abstract = "In this work, we study reinforcement learning (RL) in solving text-based games. We address the challenge of combinatorial action space, by proposing a confidence-based self-imitation model to generate action candidates for the RL agent. Firstly, we leverage the self-imitation learning to rank and exploit past valuable trajectories to adapt a pre-trained language model (LM) towards a target game. Then, we devise a confidence-based strategy to measure the LM{'}s confidence with respect to a state, thus adaptively pruning the generated actions to yield a more compact set of action candidates. In multiple challenging games, our model demonstrates promising performance in comparison to the baselines.",
}
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%0 Conference Proceedings
%T Self-imitation Learning for Action Generation in Text-based Games
%A Shi, Zijing
%A Xu, Yunqiu
%A Fang, Meng
%A Chen, Ling
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F shi-etal-2023-self
%X In this work, we study reinforcement learning (RL) in solving text-based games. We address the challenge of combinatorial action space, by proposing a confidence-based self-imitation model to generate action candidates for the RL agent. Firstly, we leverage the self-imitation learning to rank and exploit past valuable trajectories to adapt a pre-trained language model (LM) towards a target game. Then, we devise a confidence-based strategy to measure the LM’s confidence with respect to a state, thus adaptively pruning the generated actions to yield a more compact set of action candidates. In multiple challenging games, our model demonstrates promising performance in comparison to the baselines.
%R 10.18653/v1/2023.eacl-main.50
%U https://aclanthology.org/2023.eacl-main.50
%U https://doi.org/10.18653/v1/2023.eacl-main.50
%P 703-726
Markdown (Informal)
[Self-imitation Learning for Action Generation in Text-based Games](https://aclanthology.org/2023.eacl-main.50) (Shi et al., EACL 2023)
ACL